[1] "Region"         "Year"           "Wine"           "Beer"          
 [5] "Vodka"          "Sparkling_wine" "Brandy"         "Cider"         
 [9] "Liqueurs"       "Consumption"   

# A tibble: 595 × 10
   Region           Year  Wine  Beer Vodka Sparkling_wine Brandy Cider Liqueurs
   <chr>           <dbl> <dbl> <dbl> <dbl>          <dbl>  <dbl> <dbl>    <dbl>
 1 Belgorod Oblast  2017  1.79  60    3.52           0.78   0.31  1.52     0.14
 2 Bryansk Oblast   2017  3.02  34.2  4.29           0.83   0.46  0.86     0.69
 3 Vladimir Oblast  2017  3.7   58.9  6.73           0.91   0.78  1.6      0.54
 4 Voronezh Oblast  2017  2.68  33.2  3.69           1.04   0.6   0.91     0.32
 5 Ivanovo Oblast   2017  3.42  41.0  5.69           0.83   0.6   1.36     0.61
 6 Kaluga Oblast    2017  3.98  41.8  6.21           1.24   0.86  0.79     0.61
 7 Kostroma Oblast  2017  3.43  48.6  7.68           0.85   0.6   2.52     0.62
 8 Kursk Oblast     2017  2.16  30.9  3.66           0.99   0.5   0.75     0.27
 9 Lipetsk Oblast   2017  2.59  41.5  4.03           0.9    0.51  0.71     0.35
10 Moscow Oblast    2017  5.82  48.3  7.61           1.98   1.39  0.86     0.62
# ℹ 585 more rows
# ℹ 1 more variable: Consumption <dbl>

library(plotly)
df <- data.frame(x=c(1, 2, 3, 4), y=c(1, 5, 3, 5), group=c('A', 'A', 'B', 'B'))
p <- ggplot(data=df, aes(x=x, y=y, colour=group)) + geom_point()
ggplotly(p)

---
title: "Consumption"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
    social: menu
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(knitr)
library(DT)
df <- read_csv('Consumption.csv')

# Create a ggplot object
p <- df %>% group_by(Year) %>% summarise(wine_average=mean(Wine)) %>% ggplot()+geom_col(aes(x=Year,y=wine_average,fill=wine_average))

p1 <- df %>% group_by(Year) %>% summarise(beer_average=mean(Beer)) %>% ggplot()+geom_col(aes(x=Year,y=beer_average,fill=beer_average))
```

```{r}
names(df)
```

{.sidebar}
=======================================================================

### 1. Titanic

Alcohol consumption is popular amongst Europeans. When looking at the data, it has become clear that certain types of alcohol have been more popular than others.

### 2. Flexdashboard and Plotly

This interactive uses `flexdashboard` and `plotly` to visualize the data. 

Main Tab 1
=======================================================================

Column {data-width=500, .tabset}
-----------------------------------------------------------------------

### Column Tab 1

```{r}
df
```


### Column Tab 2

```{r}
kable(df)
```


### Column Tab 3

```{r}
datatable(df, options = list(
  pageLength = 25
))
```


Column {data-width=500}
-----------------------------------------------------------------------

### Row 1

```{r}
p
```

### Row 2

```{r}
ggplotly(p)
```


Main Tab 2
=======================================================================

Column {data-width=500}
-----------------------------------------------------------------------

#### 1. Plotly for R

Plotly is an R package for creating interactive web-based graphs via plotly's JavaScript graphing library, plotly.js.

The plotly R package serializes ggplot2 figures into Plotly's universal graph JSON. plotly::ggplotly will crawl the ggplot2 figure, extract and translate all of the attributes of the ggplot2 figure into JSON (the colors, the axes, the chart type, etc), and draw the graph with plotly.js. Furthermore, you have the option of manipulating the Plotly object with the style function.


#### 2. Cutomizing the Layout

Since the ggplotly() function returns a plotly object, we can manipulate that object in the same way that we would manipulate any other plotly object. A simple and useful application of this is to specify interaction modes, like plotly.js' layout.dragmode for specifying the mode of click+drag events.


#### 3. Example

```{r, echo=TRUE, eval=TRUE}
library(plotly)
df <- data.frame(x=c(1, 2, 3, 4), y=c(1, 5, 3, 5), group=c('A', 'A', 'B', 'B'))
p <- ggplot(data=df, aes(x=x, y=y, colour=group)) + geom_point()
ggplotly(p)
```



Column {data-width=500}
-----------------------------------------------------------------------

### Row 1

```{r}
p1
```

### Row 2

```{r}
ggplotly(p1)
```

M